#install.packages("NbClust")
#install.packages("mclust")
#install.packages("factoextra")
#install.packages("DiagrammeR")
#install.packages("rfUtilities")Supervised Machine Learning
Installing packages
1 Introduction to Machine Learning
1.1 What is Machine Learning?
1.1.1 Definition (Plain language):
Machine Learning is a field of study that gives computers the ability to learn from data and make predictions or decisions without being explicitly programmed for each specific task.
1.1.2 Definition (Technical):
Machine Learning is the study of algorithms and statistical models that computer systems use to learn patterns from data and make predictions or classifications based on that learning.
1.2 Types of Machine Learning
ML algorithms are generally classified into three main categories, based on the type of feedback they receive from the data:
1.2.1 Supervised Learning
What: Learn from labeled data (input-output pairs)
Goal: Predict output from new inputs
Examples:
- Regression (predict continuous values)
- Classification (predict categories)
| Task | Algorithms |
|---|---|
| Regression | Linear regression, LASSO, Ridge, SVR, Random Forest Regressor |
| Classification | Logistic regression, K-NN, Decision Trees, Random Forest, SVM, Gradient Boosting, Naive Bayes |
1.2.2 Unsupervised Learning
What: Learn patterns or structure from unlabeled data
Goal: Find hidden patterns, groups, or structures
Examples:
- Clustering
- Dimensionality Reduction
| Task | Algorithms |
|---|---|
| Clustering | K-Means, DBSCAN, Hierarchical Clustering |
| Dim. Reduction | PCA, t-SNE, UMAP |
1.2.3 Reinforcement Learning (Optional for intro)
What: Learn by interacting with an environment and receiving feedback in the form of rewards or penalties
Goal: Learn optimal decision strategies
Examples: Q-Learning, Deep Q-Networks (DQN)
1.3 The Machine Learning Workflow: A Step-by-Step Guide
Below is a typical ML workflow that mirrors real-world projects. It’s helpful to explain what is done and why at each stage.
1.3.1 Step 1: Define the Problem
Is it regression? classification? clustering?
What’s the goal of the prediction or analysis?
1.3.2 Step 2: Collect and Understand the Data
Look at variable types, distributions, missing values
Ask: Is the dataset balanced? What’s the sample size?
1.3.3 Step 3: Preprocess the Data
Clean missing values, remove duplicates
Encode categorical variables (e.g., one-hot encoding)
Scale features (e.g., standardization)
Create new features (feature engineering)
1.3.4 Step 4: Split the Dataset
Training set: used to train the model
Test set: used to evaluate final model
Optional: use a validation set or cross-validation
1.3.5 Step 5: Choose a Model
Match the algorithm to your problem type (classification, regression, etc.)
Start simple (e.g., logistic or linear regression), then try more complex ones (e.g., tree-based methods)
1.3.6 Step 6: Train the Model
Fit the model on the training data
Use cross-validation to assess performance and tune parameters
1.3.7 Step 7: Evaluate the Model
For classification: accuracy, precision, recall, F1-score, ROC-AUC
For regression: RMSE, MAE, \(R^2\)
Visualize confusion matrix, residual plots, etc.
1.3.8 Step 8: Tune Hyperparameters
- Use techniques like:
- Grid search
- Random search
- Bayesian optimization
1.3.9 Step 9: Deploy or Interpret the Model
Deploy to a production environment
Or: explain the model using SHAP, LIME, feature importance
1.3.10 Step 10: Monitor and Maintain
Monitor prediction quality over time
Re-train when new data becomes available
2 Fundamentals of ML
2.1 A Gentle Introduction | Video (12:44)
2.2 Confusion Matrix | Video (7:12)
2.3 Sensitivity and Specificity | Video (11:46)
2.4 Expected Value | Video (13:39)
2.5 Entropy | Video (16:34)
3 Regression Trees: Boston Housing Data
3.1 Decision Trees | Video (17:21)
3.2 Regression Trees | Video (22:32)
3.3 What Are Regression Trees?
3.3.1 Definition
A regression tree is a supervised learning algorithm used to predict continuous outcomes. It works by recursively splitting the dataset into branches based on feature values, producing a tree-like model where each leaf gives a predicted value (typically the average outcome in that region).
3.3.2 Intuition
Imagine a game of “20 Questions” where each question narrows down the value you’re trying to predict. A regression tree asks yes/no (or inequality) questions about input features (e.g., “Is income > $50,000?”). Each answer leads to a different branch, and at the end (leaf node), you predict the mean of observed outcomes in that group.
3.4 When Should You Use Regression Trees?
3.4.1 Ideal Scenarios:
Your outcome is continuous.
You expect nonlinear relationships between predictors and the outcome.
You want interpretable results with “if-then” logic.
You have a mix of categorical and numerical features.
You want a model that requires little preprocessing (no scaling, encoding).
3.4.2 When Not Ideal:
You need the highest predictive accuracy.
Your dataset is very small and prone to overfitting.
You need smooth, stable predictions (trees can be unstable).
3.5 How Regression Trees Work
3.5.1 Splitting
- At each node, the algorithm selects the feature and threshold that minimizes Residual Sum of Squares (RSS):
3.5.2 Recursion
- The algorithm recursively splits the data, building branches until a stopping rule is met (e.g., min node size, max depth).
3.5.3 Prediction
- For a new observation, the tree traverses nodes based on the observation’s values and returns the mean value of the terminal (leaf) node.
3.5.4 Pruning
Trees often overfit. To prevent this:
Pre-prune: Stop growing early (e.g., limit depth)
Post-prune: Grow full tree, then cut back using cross-validation
3.6 Typical Workflow for Using Regression Trees
3.6.1 Step 1: Define the Problem
- Is it a regression problem (continuous outcome)?
3.6.2 Step 2: Data Preparation
Handle missing values (trees can often handle them natively)
No need for scaling or dummy coding
3.6.3 Step 3: Split the Data
- Training and test sets (and optionally validation set or cross-validation)
3.6.4 Step 4: Fit the Model
library(rpart)
model <- rpart(outcome ~ predictors, data = train_data, method = "anova")3.6.5 Step 5: Predict and Evaluate
preds <- predict(model, newdata = test_data) rmse <- sqrt(mean((test_data$y - preds)^2))3.6.6 Step 6: Visualize Tree
rpart.plot::rpart.plot(model)3.7 Comparison to Other Methods
| Feature | Regression Tree | Linear Regression | Random Forest / Boosting |
|---|---|---|---|
| Captures nonlinearity | ✅ Yes | ❌ No (unless transformed) | ✅ Yes |
| Handles feature interaction | ✅ Implicitly | ❌ No (needs manual terms) | ✅ Very well |
| Needs feature scaling | ❌ No | ✅ Yes | ❌ No |
| Easy to interpret | ✅ Yes (rules) | ✅ Yes (coefficients) | ❌ No |
| Accuracy | ⚪ Moderate | ⚪ Moderate | ✅ High |
| Overfitting risk | ✅ High if unpruned | ✅ High | ❌ Lower |
3.8 Enhancements & Variants
Random Forests: Ensembles of many trees with bootstrapped samples
Gradient Boosting: Sequential trees fix errors from previous ones
Model Trees: Fit linear models at leaves
Oblique Trees: Use linear combinations to split, not single features
3.9 Limitations and Pitfalls
Overfitting: Deep trees can memorize training data
Instability: Small changes in data can produce very different trees
Bias in split selection: Variables with more values are favored
Poor extrapolation: Trees can’t predict beyond seen feature ranges
3.10 Use Cases
| Domain | Example Task |
| Real estate | Predict housing prices from features |
| Education | Predict student performance based on inputs |
| Marketing | Forecast customer spending |
| Healthcare | Estimate patient risk scores |
3.11 Regression Trees & Random Forest
data("Boston")
set.seed(123)
# default RF model: ntree = 500, mtry = nos. of features/3
m1 <- randomForest(formula = log(medv) ~ ., data = Boston, importance=TRUE)
plot(m1)which.min(m1$mse)[1] 500
# Tuning mtry
ForestTune <- caret::train(y=log(Boston[,14]),
x=Boston[,1:13],
tuneGrid=data.frame(mtry=1:10),
method="rf",
ntree=500,
trControl=trainControl(method="oob"))
ForestTuneRandom Forest
506 samples
13 predictor
No pre-processing
Resampling results across tuning parameters:
mtry RMSE Rsquared
1 0.1970465 0.7671552
2 0.1592069 0.8479968
3 0.1492000 0.8665045
4 0.1448385 0.8741952
5 0.1435082 0.8764956
6 0.1442932 0.8751408
7 0.1456864 0.8727179
8 0.1450441 0.8738378
9 0.1482671 0.8681688
10 0.1481130 0.8684425
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was mtry = 5.
plot(ForestTune)m1 <- randomForest(formula = log(medv) ~ ., data = Boston,
ntree=500, mtry = 5, importance=TRUE)
importance(m1) %IncMSE IncNodePurity
crim 22.045081 10.2428053
zn 5.261208 0.2064273
indus 10.185472 2.2999356
chas 3.123448 0.2268523
nox 26.029882 8.4265352
rm 41.678157 16.5194111
age 17.562822 1.9765128
dis 21.341995 4.5182532
rad 7.947701 0.6057602
tax 13.035036 2.5129642
ptratio 14.317925 4.3062644
black 15.466762 1.6596508
lstat 38.190017 29.1222391
varImpPlot(m1)plot(m1)which.min(m1$mse)[1] 492
# Partial dependance plots
par(mfrow=c(1,1))
# Partial plots of single variable
m1 %>%
partial(pred.var = "rm") %>%
plotPartial(smooth = FALSE, lwd=3,
ylab = "Median log Price",
main = "Partial Dependance Plot",
xlab ="Number of Rooms")partial(m1, pred.var = "rm", plot = TRUE, rug = TRUE) #rug: partialPlot(m1, pred.data = Boston, x.var = "rm", rug = TRUE)# Partial plots of two variables
pd <- partial(m1, pred.var = c("lstat", "rm"))
plotPartial(pd)# Add contour lines and use a different color palette
rwb <- colorRampPalette(c("red", "white", "blue")) # red=low price; blue=high price
plotPartial(pd, contour = TRUE, col.regions = rwb,
main ="PDP: % lower status pop vs nos. room",
xlab ="% lower status of Pop" ,
ylab="number of rooms")4 Gradient Boosting - Boston Housing Data
4.1 AdaBoost | Video (20:53)
4.2 Gradient Boost Part 1/4: Regression Main Ideas | Video (15:51)
4.3 Gradient Boost Part 2 (of 4): Regression Details | Video (26:45)
4.4 Gradient Boost Part 3 (of 4): Classification | Video (17:02)
4.5 Gradient Boost Part 4 (of 4): Classification Details | Video (36:59)
set.seed(123)
# train GBM model
gbm1 <- gbm(
formula = log(medv) ~ .,
distribution = "gaussian",
data = Boston,
n.trees = 2000,
interaction.depth = 1,
shrinkage = 0.001,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
# Using caret with the default grid to optimize tune parameters automatically
# GBM Tuning parameters:
# n.trees (# Boosting Iterations)
# interaction.depth (Max Tree Depth)
# shrinkage (Shrinkage)
# n.minobsinnode (Min. Terminal Node Size)
# Tuning hyper hyper parameters
hyper_grid <- expand.grid(
n.trees = c(1000, 2000),
shrinkage = c(0.001, .01, .05),
interaction.depth = c(1, 2, 3),
n.minobsinnode = c(10, 20))
trainControl <- trainControl(method="cv", number=10)
set.seed(99)
gbm.caret <- train(log(medv) ~ .
, data=Boston
, distribution="gaussian"
, method="gbm"
, trControl=trainControl
, verbose=FALSE
, tuneGrid=hyper_grid
)
# attributes(gbm.caret)
gbm.caret$bestTune n.trees interaction.depth shrinkage n.minobsinnode
30 2000 2 0.05 10
# GBM with tunes hyper parameters
gbm2 <- gbm(
formula = log(medv) ~ .,
distribution = "gaussian",
data = Boston,
n.trees = 2000,
interaction.depth = 2,
shrinkage = 0.05,
n.minobsinnode = 10,
cv.folds = 5,
n.cores = NULL, # will use all cores by default
verbose = FALSE
)
# Variable importance
vip::vip(gbm2)# PDPs
gbm2 %>%
partial(pred.var = "rm", n.trees = gbm2$n.trees, grid.resolution = 100) %>%
autoplot(rug = TRUE, train = Boston)gbm2 %>%
partial(pred.var = c("rm", "lstat"), n.trees = gbm2$n.trees, grid.resolution = 100) %>%
autoplot(rug = TRUE, train = Boston)pd <- partial(gbm2, pred.var = c("rm","lstat"), n.trees = gbm2$n.trees, grid.resolution = 100)
# Add contour lines and use a different color palette
rwb <- colorRampPalette(c("red", "white", "blue"))
plotPartial(pd, contour = TRUE, col.regions = rwb,
main ="PDP: % lower status pop vs nos. room", xlab="% lower status of Pop", ylab ="number of rooms")5 Classification Trees for Buy-No Buy Data: Eight product categories
5.1 Decision and Classification Trees | Video (18:08)
data1 = read.csv(here("data", "iridata.csv"))
# Baby,Coffee,Detergent,Paste,Sauce,Snacks,Sunscreen,Tuna
data2 = subset(data1, subset=catno =="Baby")
str(data2)'data.frame': 1225 obs. of 12 variables:
$ catno : chr "Baby" "Baby" "Baby" "Baby" ...
$ hhinc : chr "$100 plus" "$25-49K" "$100 plus" "< 25K" ...
$ hhsize : chr "1-2 members" "3 members" "1-2 members" "1-2 members" ...
$ race : chr "Other" "Caucasian" "Caucasian" "Caucasian" ...
$ ethnicity : chr "Non-Hispanic" "Non-Hispanic" "Non-Hispanic" "Non-Hispanic" ...
$ affluence : chr "Getting By" "Living Comfortably" "Getting By" "Doing Well" ...
$ pres_child : chr "No" "No" "No" "No" ...
$ age_head : chr "55-64Y" "50-54Y" "65+Y" "65+Y" ...
$ educ_fem_head: chr "Some College" "Some or High School" "Post College Grade" "Some or High School" ...
$ census : chr "West" "South" "West" "Central" ...
$ buy : int 0 0 0 0 0 0 0 0 0 0 ...
$ purchase : chr "NO" "NO" "NO" "NO" ...
cat_vars <- c("hhinc", "hhsize", "race", "ethnicity", "affluence", "pres_child",
"age_head", "educ_fem_head", "census", "purchase")
data <- data2 %>%
mutate(across(all_of(cat_vars), as_factor))
str(data)'data.frame': 1225 obs. of 12 variables:
$ catno : chr "Baby" "Baby" "Baby" "Baby" ...
$ hhinc : Factor w/ 5 levels "$100 plus","$25-49K",..: 1 2 1 3 3 4 2 1 4 2 ...
$ hhsize : Factor w/ 3 levels "1-2 members",..: 1 2 1 1 1 3 1 3 1 3 ...
$ race : Factor w/ 2 levels "Other","Caucasian": 1 2 2 2 2 2 1 2 2 2 ...
$ ethnicity : Factor w/ 2 levels "Non-Hispanic",..: 1 1 1 1 1 1 1 1 1 1 ...
$ affluence : Factor w/ 3 levels "Getting By","Living Comfortably",..: 1 2 1 3 3 1 1 1 1 3 ...
$ pres_child : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 2 1 2 1 2 ...
$ age_head : Factor w/ 6 levels "55-64Y","50-54Y",..: 1 2 3 3 2 4 1 1 1 5 ...
$ educ_fem_head: Factor w/ 4 levels "Some College",..: 1 2 3 2 2 2 1 3 2 2 ...
$ census : Factor w/ 4 levels "West","South",..: 1 2 1 3 1 1 2 3 2 3 ...
$ buy : int 0 0 0 0 0 0 0 0 0 0 ...
$ purchase : Factor w/ 2 levels "NO","YES": 1 1 1 1 1 1 1 1 1 1 ...
# Single Tree: Tuning complexity parameter, cp
TreeTune<-train(y=data[,12],
x=data[,2:10],
tuneGrid=data.frame(cp=seq(.001,.05,length.out = 20)),
method="rpart",
trControl=trainControl(method="repeatedcv",repeats=10,number=10))
TreeTune$bestTune cp
7 0.01647368
plot(TreeTune)# Tree
GoodTree <- rpart(purchase ~ hhinc + hhsize + race + ethnicity +
affluence + pres_child + age_head + educ_fem_head +
census,data=data,method="class",
control=rpart.control(cp=0.01389474))
# summary(GoodTree)
fancyRpartPlot(GoodTree)onetree <- predict(GoodTree, type="prob")[,2]
rpart.plot(GoodTree, type =5, extra=104, fallen.leaves=T, main="Sample Single Tree")6 Random Forest NOBuy- Buy Data
6.1 Random Forests Part 1| Video (9:53)
6.2 Random Forests Part 2 | Video (11:52)
# Tuning mtry
ForestTune <- caret::train(y=data[,12],
x=data[,2:10],
tuneGrid=data.frame(mtry=1:6),
method="rf",ntree=500,
trControl=trainControl(method="oob"))
ForestTune$results Accuracy Kappa mtry
1 0.6653061 0.1179931 1
2 0.6693878 0.2433716 2
3 0.6465306 0.1980087 3
4 0.6391837 0.1904641 4
5 0.6359184 0.1865918 5
6 0.6293878 0.1789447 6
plot(ForestTune)m2 <- randomForest(x=data[,2:10],y=data[,12],data=data,
type="classification",ntree=500,mtry=2, importance = TRUE)
# Random Forest prediction
rfpred <- predict(m2,type="prob")[,2]
importance(m2) NO YES MeanDecreaseAccuracy MeanDecreaseGini
hhinc 9.9518220 -0.3497756 7.2112029 35.142585
hhsize 10.9960555 10.5337820 18.7930710 29.110216
race 1.4224556 0.8806341 1.5690573 12.071906
ethnicity 0.1589872 -3.6983692 -3.0587445 8.718574
affluence 3.7526294 -0.8801165 2.3765147 17.028193
pres_child 10.4635617 13.8523152 24.9207270 25.745736
age_head 25.9393002 11.2475624 29.6558953 70.898330
educ_fem_head -5.1915254 4.2517352 -0.1875437 31.587706
census -1.9234697 1.0663672 -0.3993511 32.299178
varImpPlot(m2)# PDP for single variable
partialPlot(x=m2, pred.data=data, x.var=hhsize, which.class="YES")partialPlot(x=m2, pred.data=data, x.var=pres_child, which.class="YES")# Household Size
m2 %>%
partial(pred.var="hhsize", prob=TRUE, which.class ="YES" ) %>%
plotPartial(smooth=FALSE, lwd=2, ylab=expression(probability),ylim=c(0.5,0.9), main=expression(Partial.Dependance.Plot(Random.Forest)), xlab=expression(Household.Size))# Age of Head
m2 %>%
partial(pred.var="pres_child", prob=TRUE, which.class ="YES" ) %>%
plotPartial(smooth=TRUE, lwd=2, ylab=expression(probability), ylim=c(0.1,1), main=expression(Partial.Dependance.Plot(Random.Forest)), xlab=expression(Age.Head))# PDP for two variables
m2 %>%
partial(pred.var=c("pres_child", "hhinc"), prob=TRUE, which.class ="YES") %>%
plotPartial(smooth=TRUE, lwd=2, ylab=expression(probability), ylim=c(0.1,1), main=expression(Partial.Dependance.Plot(Random.Forest)), xlab=expression(Presence.Child))7 Random Forest: Predictive Accuracy in IRI data
set.seed(1001)
indxTrain <- createDataPartition(y = data$purchase,p = 0.70,list = FALSE)
training <- data[indxTrain,]
testing <- data[-indxTrain,]
rfarrest <- randomForest(x=training[,2:10],y=training[,12],data=training,
type="classification",ntree=1000,mtry=2, importance = TRUE)
rfarrest$confusion NO YES class.error
NO 126 179 0.5868852
YES 97 456 0.1754069
# Predictive accuracy in training sample
accuracy.train <- rfarrest %>% predict(training)
confusionMatrix(accuracy.train, training$purchase)Confusion Matrix and Statistics
Reference
Prediction NO YES
NO 212 32
YES 93 521
Accuracy : 0.8543
95% CI : (0.8289, 0.8773)
No Information Rate : 0.6445
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6671
Mcnemar's Test P-Value : 8.025e-08
Sensitivity : 0.6951
Specificity : 0.9421
Pos Pred Value : 0.8689
Neg Pred Value : 0.8485
Prevalence : 0.3555
Detection Rate : 0.2471
Detection Prevalence : 0.2844
Balanced Accuracy : 0.8186
'Positive' Class : NO
# Predictive accuracy in testing sample
accuracy.test <- rfarrest %>% predict(testing)
confusionMatrix(accuracy.test, testing$purchase)Confusion Matrix and Statistics
Reference
Prediction NO YES
NO 50 47
YES 80 190
Accuracy : 0.654
95% CI : (0.6028, 0.7026)
No Information Rate : 0.6458
P-Value [Acc > NIR] : 0.394399
Kappa : 0.1976
Mcnemar's Test P-Value : 0.004518
Sensitivity : 0.3846
Specificity : 0.8017
Pos Pred Value : 0.5155
Neg Pred Value : 0.7037
Prevalence : 0.3542
Detection Rate : 0.1362
Detection Prevalence : 0.2643
Balanced Accuracy : 0.5932
'Positive' Class : NO
8 XGBoot: Conceptual Understanding
8.1 XGBoost Part 1 (of 4): Regression | Video (25:45)
8.2 XGBoost Part 2 (of 4): Classification | Video (25:17)
8.3 XGBoost Part 3 (of 4): Mathematical Details | Video (27:23) (Optional)
8.4 Quantiles and Percentiles | Video (6:29)
8.5 XGBoost Part 4 (of 4): Crazy Cool Optimizations | Video (24:26)
9 Extreme Gradient Boosting: IRI data, Buy or NO BUy
# create tuning grid
grid_default <- expand.grid(nrounds = c(200,300),
max_depth = c(4,5), eta = c(0.01), gamma = c(0,1), min_child_weight = c(10, 25),
colsample_bytree = c(0.7), subsample = c(0.6))
set.seed(123)
# train XGBoost model
xgboost2 <- train(purchase ~ hhinc + hhsize + race + ethnicity +
affluence + pres_child + age_head + educ_fem_head +
census,data=data, tuneGrid = grid_default,
method = "xgbTree", metric ="Accuracy")[09:18:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
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[09:18:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:18:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:18:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:18:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:19:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:20:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
plot(xgboost2)xgboost2$bestTune nrounds max_depth eta gamma colsample_bytree min_child_weight subsample
9 200 5 0.01 0 0.7 10 0.6
xgb.plot.tree(model = xgboost2$finalModel,
trees = 1, plot_width = 800, plot_height = 800)importance_matrix <- xgb.importance(model = xgboost2$finalModel)
print(importance_matrix) Feature Gain Cover Frequency
<char> <num> <num> <num>
1: pres_childYes 0.313255945 0.149982238 0.07957560
2: age_head< 35Y 0.213512031 0.140750397 0.08885942
3: age_head65+Y 0.071533380 0.073617366 0.06100796
4: hhsize4 or more 0.056827456 0.057961415 0.05238727
5: age_head50-54Y 0.034538342 0.046493002 0.04045093
6: educ_fem_headSome or High School 0.031720721 0.056185679 0.06432361
7: raceCaucasian 0.031277181 0.048490198 0.06366048
8: age_head35-44Y 0.028896302 0.041576967 0.05238727
9: age_head45-49Y 0.027824912 0.034404236 0.02188329
10: censusSouth 0.024539377 0.038736697 0.07957560
11: hhsize3 members 0.021220160 0.033321589 0.03580902
12: hhinc$70-99K 0.021092874 0.036584409 0.04310345
13: censusEast 0.019982536 0.031279268 0.03183024
14: educ_fem_headGradauted College 0.016224577 0.036359556 0.06432361
15: educ_fem_headPost College Grade 0.016079962 0.030798136 0.03050398
16: hhinc$50-69K 0.015814372 0.028411720 0.03116711
17: affluenceLiving Comfortably 0.014619607 0.027782896 0.04376658
18: censusCentral 0.014351058 0.029615329 0.04177719
19: hhinc$25-49K 0.013456453 0.026161236 0.03912467
20: affluenceDoing Well 0.008700676 0.019426143 0.02453581
21: ethnicityHispanic 0.002826491 0.006637517 0.00397878
22: hhinc< 25K 0.001705586 0.005424006 0.00596817
Feature Gain Cover Frequency
xgb.plot.importance(importance_matrix = importance_matrix)10 Extreme Gradient Boosting: nhefs data
nhefs <- read_csv(here("data", "nhefs.csv")) %>%
mutate(wt_delta = as.numeric(wt82_71 > median(wt82_71)))
# create tuning grid
grid_default <- expand.grid(nrounds = c(200,300),
max_depth = c(4,5), eta = c(0.01), gamma = c(0,1), min_child_weight = c(10, 25),
colsample_bytree = c(0.7), subsample = c(0.6))
set.seed(123)
# train XGBoost model
xgboost1 <- train(factor(wt_delta) ~ qsmk +
sex + age + income + sbp + dbp + price71 +
tax71 + race, data = nhefs, tuneGrid = grid_default,
method = "xgbTree", metric = "Accuracy")[09:21:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:21:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:22:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:23:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
[09:24:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
# plot(xgboost1)
xgboost1$bestTune nrounds max_depth eta gamma colsample_bytree min_child_weight subsample
15 200 5 0.01 1 0.7 25 0.6
xgb.plot.tree(model = xgboost1$finalModel,
trees = 1:2, plot_width = 800, plot_height = 800)importance_matrix <- xgb.importance(model = xgboost1$finalModel)
print(importance_matrix) Feature Gain Cover Frequency
<char> <num> <num> <num>
1: age 0.458471328 0.32119934 0.281146637
2: dbp 0.153948154 0.16985337 0.171995590
3: qsmk 0.140593604 0.14179957 0.108048512
4: sbp 0.096442882 0.13536373 0.153252481
5: price71 0.061374316 0.09701480 0.127894157
6: income 0.046092537 0.06665409 0.072767365
7: sex 0.022381198 0.03164424 0.044101433
8: tax71 0.015433740 0.02554422 0.033076075
9: race 0.005262242 0.01092664 0.007717751
xgb.plot.importance(importance_matrix = importance_matrix)